In today's digital age, many enterprises face many difficulties when it comes to data management. Some enterprises have chaotic data, but they don't know where to start sorting it out; Some companies are overwhelmed by the sheer volume of data they have to offer.
Data lineage analysis is a key tool to solve these pain points. Data lineage analysis is like a doctor diagnosing a disease, and only by accurately finding out the cause can we prescribe the right medicine. If enterprises do not conduct data lineage analysis and move forward blindly, they will not only waste a lot of time and resources, but also may make wrong decisions, which will bring huge losses to the enterprise.
Before enterprises carry out digital transformation, they must understand the importance of data lineage analysis. It is necessary to clarify the pain points, itch points, pain points, and expectations of the data. The pain point lies in the confusion and unreliability of data, the itch point lies in how to better use data to improve business efficiency, the difficulty lies in how to carry out effective data lineage analysis, and the expectation point is to realize the visualization and manageability of data through data lineage analysis. At the same time, it is necessary to find the entry point of data lineage analysis. You can start with business-critical data and gradually expand to other data areas.
In short, data lineage analysis is an indispensable part of enterprise digital transformation. Only through data lineage analysis can enterprises better understand and manage data, laying a solid foundation for digital transformation.
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1. What is data lineage?
Data lineage, also known as data lineage, data origin, and data lineage, refers to a relationship similar to human lineage naturally formed between data in the whole life cycle of data, from data generation, processing, processing, fusion, circulation to eventual extinction. To put it simply, it is the relationship between the upstream and downstream sources of data - where the data comes from and where it goes. Data lineage involves not only the physical flow of data, but also the logical relationship and transformation process of data.
Data lineage is critical to understanding where data comes from, how it's processed, how it's mapped, and where it exits. It helps enterprises better manage data assets, ensure data quality and security, and also help troubleshoot and solve data problems.
2. Four characteristics of data kinship
- Attribution: In general, specific data is attributed to a specific organization or individual.
- Multi-source: the same data can have multiple sources (multiple parents); A piece of data can also be generated by processing multiple pieces of data, and this processing process can be multiple.
- Traceability: The lineage of the data reflects the life cycle of the data, and reflects the entire process of the data from generation to death, with traceability.
- Hierarchical: There is a hierarchy of kinship in the data. The descriptions of data such as classification, induction, and summary will form new data, and different degrees of descriptive information will form a hierarchy of data.
3. How to do data lineage analysis?
Data lineage analysis is one of the important applications of metadata management, and its process can be roughly divided into the following steps:
1. Define a metadata model
Identify the types of metadata that need to be managed, such as database tables, fields, ETL procedures, data warehouse models, and so on. Define the properties of metadata, including name, description, data type, source, destination, and so on.
2. Collect metadata
Extract metadata from a variety of data sources, such as databases, data warehouses, ETL tools, data lakes, and more. Automate the collection of metadata with a metadata extraction tool or service.
3. Model kinship
Determine the type of kinship, such as upstream/downstream, paternity, dependency, etc. Design a lineage graph model to graphically represent the relationships between metadata.
4. Track the flow of data
Implement algorithms to trace the flow path of metadata, determining the complete path from one data element to another. Use graph databases or graph processing frameworks (e.g., Neo4j, Apache Giraph, Spark GraphX, etc.) to store and query lineage.
5. Visual analytics
Leverage visualization tools and technologies such as D3.js, ECharts, Tableau, and more to present kinship diagrams. Provides an interactive interface that allows users to explore and analyze kinship.
6. Integrate into data governance
Make lineage analysis an important part of data governance. Use the results of lineage analysis to develop data quality rules, data security policies, and data retention policies.
7. Ongoing Updates and Maintenance
Continuously update the lineage chart as the data environment changes, such as the addition of new data sources, the modification of data processes, etc. Monitor the accuracy and completeness of data lineage to ensure the reliability of analysis results.
8. Apply the results of the analysis
Use the analysis results to analyze the impact of data changes and evaluate the potential impact of data quality issues and data security incidents on related data. Optimize the data flow based on the analysis results to improve the efficiency and quality of data use.
Fourth, the trend of data lineage technology
In the industry, the development trend of kinship mainly focuses on the following points:
1. Universal lineage analysis ability
Lineage is the core capability of the metadata platform, many times the metadata platform will access diversified metadata, these business metadata will also rely on the ability to parse the lineage of different bloodlines, the current parsing often relies on various engine teams to support, but in fact, in a wider range of scenarios, we need to have a comprehensive solution to provide a more general physique parsing capability, so in the future we will provide a standard SQL parsing engine to achieve the purpose of general parsing.
2. Non-intrusive, non-SQL type lineage
In addition to parsing SQL or configurable tasks, there are also tasks that involve code types such as JAR tasks on a day-to-day basis. The current parsing method of JAR tasks is to complete the collection of lineage according to some buried information or upstream and downstream information entered by users, and there will be a non-intrusive non-SQL type lineage collection technology in the future, such as the JAR task of Flink or Spark, we can get these lineages when the task is running, to enrich the data of lineage on the platform side.
3. Chronological kinship
For example, when a user modifies a task, the online task changes or modifies the table structure, and then modifies the production task accordingly, which involves the concept of time series, which can facilitate us to trace the changes of some tasks and support us to do pre- and post-event impact analysis, so how to introduce time series lineage into the graph database is also a trend in the future.
To clarify the kinship of data is to clarify the upstream and downstream sources and destination relationships between data. By building a comprehensive and accurate full-link data lineage view, enterprises can find out the upstream and downstream applications of data, speed up the debugging speed of business data errors reported by the data department, and reduce enterprise decision-making errors. It can also take long-term useless databases or reports offline in a timely manner to save data management costs.
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